BiShield-TEE：端侧 TEE 保护 LLM 分区中单向权重混淆的安全性缺陷与双边防御

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Abstract:On-device deployment of large language models (LLMs) is increasingly adopted due to latency and privacy requirements, but exposes proprietary models to reverse engineering and intellectual property theft. Trusted Execution Environments (TEEs) provide hardware-enforced isolation for on-device inference, yet most TEE-based parameter obfuscation relies on unilateral matrix transformations that obfuscate only output side. We identify a vulnerability in this design, termed the \emph{Solution Determinacy Property (SDP)}: the obfuscation secret is uniquely determined, exposing the structure of the true key and enabling attackers to break the protection. Under our attacks, representative methods \textsc{GroupCover} and \textsc{ArrowCloak} are reduced to negligible protection, with attack accuracy reaching over $2.4\times$ the full-shielding black-box baseline and recovering near-complete model this http URL address this vulnerability, we propose \textsc{BiShield-TEE}, a bilateral framework that induces \emph{Ambiguous Solutions} through joint obfuscation of input and output dimensions, hiding the true key among many equally valid candidates. Experiments across diverse models and datasets show that \textsc{BiShield-TEE} confines attack accuracy to the full-shielding black-box baseline, while achieving higher inference efficiency than prior methods on real this http URL code is available at this https URL.

Submission history

From: Zhiyuan Wang [view email]
[v1] Thu, 15 Oct 2026 09:22:45 UTC (691 KB)